P^2O method enhances LLM reasoning by optimizing prompts and policies

Researchers have developed a new method called P^2O (Joint Policy and Prompt Optimization) to address the issue of advantage collapse in Reinforcement Learning with Verifiable Rewards (RLVR) for large language models. This technique alternates between continuous policy updates and discrete prompt evolution, using the GEPA algorithm to discover effective prompts for challenging samples. By distilling these prompts into the model's parameters, P^2O improves out-of-distribution generalization and achieves up to a 9.5% performance increase over existing methods. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a novel approach to enhance LLM reasoning by combining prompt engineering with reinforcement learning, potentially improving performance on complex tasks.

RANK_REASON This is a research paper detailing a new method for improving LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• P^2O
• RLVR
• LLM
• GEPA
• Kaiqi Zhang

• paper
• other